Distributors have embraced the value of data analytics in their operations, using visualization software and machine learning to inform business practices. Distribution analytics allows companies to describe and predict their business environment with information that can be easily used and understood.
A Perfect Storm of Helpful Technology
The business value of distribution analytics is simple. It helps you know, not guess, what’s going on. In the past, businesses had to rely on the experience and intuition of their decision makers. This, unfortunately, brought along baggage in the form of personal preferences and bias. Now, distributors can get data-driven information about their current and future operations. The technology that provides this independent view helps distributors:
- Process, store and categorize huge volumes of data at lightning speed.
- Use and manage many types of data such as sensor signals from trucks and warehouses.
- Use scalable cloud-based delivery to eliminate expensive in-house IT infrastructure.
- Provide specialized, third-party analytics assistance (analytics as a service).
- Use self-service data analytics software, which requires little or no expert assistance to set up and use.
Data analytics is powerful, and it helps to know what’s going on under the hood.
Poking through the Layers of Distribution Analytics
Think of data analytics capabilities as a layer cake.
Level 0: Data.
Distributors collect and store data in workstations, relational databases, and enterprise data warehouses. That’s the most surface-level information, the frosting on top of the cake. Analytics involves getting below the surface to see what’s happening and why.
Level 1: Descriptive data analysis.
The first stage involves reporting and describing what’s in the data. For example, how many products were sold last week, last month, and last quarter? Many distributors use the reporting from their B2B Commerce software to access this granular sales and ordering data.
Level 2: Root-cause data analysis.
You don’t get value from analytics until you look for the root causes and factors that drive revenue and costs. Level 1 analysis says your customers are going nuts for a certain product. Why? Level 2 analysis tells you what’s driving demand.
Level 3: Predictive data analysis.
You’re thrilled that your customers love your products, but what should you do if this isn’t just a fad? By using root-cause and newly developed predictive analytics, decision makers can look into the future. The analysis provides real-life suggestions that will help you make the most of your opportunity.
Okay, that’s the how. But knowing what you should measure is a big part of analytics success.
Looking for Data in All the Right Places
There’s a universe of data awaiting analysis. To your company, though, only a small subset is worth measuring. Here are some tips about what to avoid and where to start. Avoid averages. Don’t waste the firepower of analytics software by limiting yourself to Level 1 queries. Unless you dig down and get to root causes, averages can be deceiving. For example, the weekly average of 3Q2016 sales of one of your products has skyrocketed. But were most of those sales made with standard pricing or on promotion at deep discounts? Are you sure? Go after the KPIs. What to measure depends on your business’ high-level business goals (critical success factors) and the key performance indicators (KPIs) used to measure them. For example, if you distribute perishable goods, do you want to improve product freshness? Then, look for the causes of delays in the shipping process.
The Benefits of Distribution Analytics
The value of distribution analytics is its ability to go beyond business intelligence-style summaries and reports. Instead, you get a data-based picture of your role in the industry. Here are three of the many value-rich capabilities that data analytics provides: A clear idea of what your customers want. You can segment customers by their buying preferences, demographics, and other characteristics to customize your queries. More efficient operations. Everyone wants faster delivery. Distribution analytics can help you improve delivery speed, which improves the customer experience. You’re also likely to experience a healthy ROI. Industry analysts have shown consistently that traditional advanced analytics implementations (large up-front investment and expensive talent) have a return on investment of 3:1 and more. By using the analytics-as-a-service delivery model, you can rent affordable analytics services and experience at an even higher ROI in a much shorter timeframe. How has distribution analytics worked for you? Did you encounter any unexpected insights? Let us know in the comments.